A Developer's Guide to Choosing Deployment Platforms for ML Projects

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Deploying a machine learning project requires selecting the right platforms for frontend, backend, and source code management. For frontend deployment, Vercel is a popular choice due to its GitHub integration, automatic deployments, and simple configuration. On the backend side, options like Hugging Face Spaces, Render, and Railway each offer varying levels of support for ML workloads, databases, and free tiers. GitHub remains the go-to tool for version control, collaboration, and connecting repositories to deployment pipelines. The author notes that the best platform ultimately depends on project-specific factors such as model size, resource requirements, traffic, and budget.

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